Football athelets’ database

• Our motivation for this project was sparked by a shared interest in sports. We had built up a dataset as a side project prior to this course by scraping data from www.pro-football-reference.com1 and www.footballoutsiders.com2 using python scripts, so we decided to use that dataset. Fantasy football is a very large industry. It is estimated that each season costs employers in the USA $17 billion dollars in productivity3. As such, there is a lot of interest in the field and people are always looking for an edge. Normally it is expensive to purchase tools (ex. ESPN API) to gain an advantage on evaluating the data. Therefore it is hard to try to interpret large amounts of data without investing the capital. We began the project with the intention to create a tool that can perform searches within our extracted data to allow a user to try to identify trends in the data.

by fantasy coders: Chan Hyeok Yun, Oliver DeBarros, Joseluis Garcia

License Plate Recognition

• Incoming Image
• Convert to Binary
• Find properties of connected Components
• Filter out undesired bounding boxes
• Compare Binary picture of letter to alphanumeric database
• Output best comparison values